Comparison of tests done, and tuberculosis cases detected by Xpert® MTB/RIF and Xpert® MTB/RIF-Ultra in Uganda

Background Uganda introduced Xpert® MTB/RIF assay into its TB diagnostic algorithm in January 2012. In July 2018, this assay was replaced with Xpert® MTB/RIF Ultra assay. We set out to compare the tests done and tuberculosis cases detected by Xpert® MTB/RIF and Xpert® MTB/RIF Ultra assay in Uganda. Methods This was a before and after study, with the tests done and TB cases detected between Jan-June 2019 when using Xpert® MTB/RIF Ultra assay compared to those done between Jan-June 2018 while using Xpert® MTB/RIF assay. This data was analyzed using Stata version 13, it was summarized into measures of central tendency and the comparison between Xpert® MTB/RIF Ultra and Xpert® MTB/RIF was explored using a two-sided T-test which was considered significant if p <0.05. Results One hundred and twelve (112) GeneXpert sites out of a possible 239 were included in the study. 128,476 (M: 1147.11, SD: 842.88) tests were performed with Xpert® MTB/RIF Ultra assay, with 9693 drug-susceptible TB (DS-TB) cases detected (M: 86.54, SD: 62.12) and 144 (M: 1.28, SD: 3.42) Rifampicin Resistant TB cases (RR-TB). Whilst 107, 890 (M: 963.30, SD: 842.88) tests were performed with Xpert® MTB/RIF assay between, 8807 (M: 78.63, SD: 53.29) DS-TB cases were detected, and 147 (M: 1.31, SD: 2.39) RR-TB cases. The Number Need to Test (NNT) to get one TB case was 12 for Xpert® MTB/RIF and 13 for Xpert ®MTB/RIF Ultra. On comparing the two assays in terms of test performance (p = 0.75) and case detection both susceptible TB (p = 0.31) and RR-TB (p = 0.95) were not found statistically significant. Conclusions This study found no significant difference in test performance and overall detection of DS-TB and RR-TB when using Xpert® MTB/RIF Ultra and Xpert® MTB/RIF assays. The health systems approach should be used to elucidate all the probable potential of Xpert® MTB/RIF Ultra.


Conclusions
This study found no significant difference in test performance and overall detection of DS-TB and RR-TB when using Xpert® MTB/RIF Ultra and Xpert® MTB/RIF assays. The health systems approach should be used to elucidate all the probable potential of Xpert® MTB/RIF Ultra.
To overcome these limitations Xpert1 MTB/RIF Ultra assay (Xpert1 Ultra) was developed and endorsed for use by WHO in 2017. Xpert1 MTB/RIF Ultra assay has several advantages over Xpert1 MTB/RIF assay, it has shown an 11-17% increase in sensitivity among smear-negative, culture-positive samples compared to the Xpert1 MTB/RIF assay [3,4]. The Xpert1 Ultra assay also has a shorter run time of 77 minutes per positive sample and 65 minutes for a negative sample when compared to the Xpert1 MTB/RIF assay's 114 minutes [9]. Therefore, by simply switching from Xpert1 MTB/RIF to Xpert1 MTB/RIF Ultra assay, programs could increase their already installed GeneXpert1 system capacity by up to 50% depending on the positivity rate [10].
Uganda introduced Xpert1 MTB/RIF assay into its TB diagnostic algorithm in January 2012. In July 2018, due to the above-mentioned reasons, this assay was replaced with the Xpert1 MTB/RIF Ultra assay. We set out to determine, the difference in the number of tests done and TB cases detected by Xpert1 MTB/RIF Ultra in comparison with Xpert1 MTB /RIF.

Study design and setting
This was a before and after study, where GeneXpert1 tests and TB cases detected between January to June 2019 were compared to those done between January to June 2018 when Xpert1 MTB/RIF Ultra and Xpert1 MTB/RIF were used, respectively. In 2018, Uganda had 239 GeneXpert1 sites randomly distributed across the then 122 districts of Uganda.
The country's health system is stratified according to levels of care from top to bottom as follows; National Referral Hospitals (NRHs), Regional Referral Hospitals (RRHs), General district hospitals (GHs), Health Centre IVs (HCIVs), Health Centre IIIs (HCIIIs) and Health Centre IIs (HCIIs). GeneXpert services are however limited to both regional and district General hospitals and some selected HCIVs and HCIIIs based on infrastructural requirements, workload, and accessibility among other factors. The country has a robust sample referral system (HUB system) where samples are picked from peripheral health facilities without GeneXpert machines to functional GeneXpert sites and the results are relayed back the same way [11].

Study population
One hundred and twelve (112) GeneXpert sites out of a possible 239 were included in the study. All the GeneXpert sites in the country did transition from Xpert MTB/RIF to Xpert MTB/RIF Ultra. GeneXpert sites that had a weekly reporting rate of 100% between January-June 2018 and January to June 2019 were included in this study, the same sites should have reported for both periods, therefore the sites were matched for both periods. They were excluded if either they did not report to the National TB and Leprosy Program (NTLP) or they broke down, hence reporting zeros.

Study description
Weekly, all GeneXpert sites in the country report GeneXpert surveillance data to the National TB and Leprosy Program. This data is collected through a standardized template that has a number of tests done, number with Drug-Sensitive TB (DS-TB), number with Rifampicin Resistant TB (RR-TB), number of children tested, and errors codes.
The GeneXpert sites that don't report are flagged by the focal person at the National TB Reference Laboratory (NTRL) and later reminded to do so. If they still don't report, then the Regional TB and Leprosy Focal Person (RTLFP) and Regional Implementing Partner (IP) are tasked with ensuring that reporting is done. These reports are then collated and presented to the various stakeholders (Ministry of Health, Development Partners, Donors, and Implementing Partners) as utilization rates (Number of tests done per day) per site. The sites with a good GeneXpert Utilization (>16 tests done per day) are applauded, while the poorly performing sites (usually < 4 tests a day) are tasked to explain the performance and improve. This data is then collated into quarterly and annual reports which make part of the annual performance report for the NTLP.

Data collection methods and procedure
We used programmatic data generated from the weekly GeneXpert surveillance reports routinely submitted to the National TB programs by GeneXpert sites with support from regional Implementing partners through emails, GX-alert systems, or phone calls. This surveillance data is collected through a standardized template that has a number of tests done, number with Drug-Sensitive TB (DS-TB), number with Rifampicin Resistant TB (RR-TB), and number of children tested, and errors codes. We collated data from the weekly GeneXpert surveillance reports into a spreadsheet (Windows Excel 2016, Microsoft Corp., Redmond, WA). The data was then subjected to the inclusion and exclusion criteria, then checked for inconsistencies, and cleaned in preparation for analysis.

Study outcomes
The primary outcomes were the tests and TB cases detected by Xpert1 MTB/RIF Ultra and Xpert1 MTB/RIF assays. While the secondary outcomes were Number Needed Test (NNT) to get one TB case, and a comparison between tests done and the number of TB cases detected (DS-TB and RR-TB) by Xpert1 MTB/RIF Ultra and Xpert1 MTB/RIF assays respectively.

Data analysis
We exported data from a spreadsheet (Microsoft Excel version 2013) into Intercooled Stata version 13 (Stata-Corp, College Station, Texas, USA). The outcome data were summarized into measures of central tendency (Standard deviation and mean) and the comparison between Xpert1 MTB/RIF Ultra and Xpert1 MTB/RIF was explored using a two-sided Ttest which was considered significant if p <0.05.

Ethical approval
The study used secondary data which was anonymous and widely available. However, approval for the study was obtained from the Operational Research and Ethics Review Committee of the Uganda National Tuberculosis and Leprosy Control Programme.

Description of the study sites
One hundred twelve (112) sites were included in the study and were distributed as follows according to the Ministry of Health Uganda Regions, which is based on the Regional Referral Hospital the facilities fall under (Kampala-15, Mubende-13, Lira-5, Gulu-8, Arua-9, Moroto-6, Masaka-6, Mbarara-15, Fort Portal-7, Hoima-6, Jinja-8, Mbale-7, and Soroti-7). During the periods before and after switching from Xpert MTB/RIF to Xpert MTB/RIF Ultra, more tests were done by Mbarara Region but most cases were detected by Kampala Region, while Lira Region did the least number of tests when MTB/RIF was being used and detected the least cases, while on switching to MTB/RIF Ultra it was Mbale Region with both the least number of tests and cases detected (See Table 1).

Test performance and TB cases detected
The test performance for Xpert1 MTB/RIF Ultra assay during the period January to  (Table 1).

Comparison between Xpert1 MTB/RIF and Xpert1 MTB/RIF Ultra
The number needed to test (NNT) to diagnose one DS-TB case was 12 for Xpert MTB/RIF and 13 for Xpert MTB/RIF Ultra, this translated into 75/1000 and 82/1000 DS-TB cases detected with Xpert1 MTB/RIF Ultra and Xpert1 MTB/RIF respectively. While both Xpert1 MTB/ RIF Ultra and MTB/RIF did detect 1 MDR-TB case per 1000 tests done. The difference between the two assays in terms of test performance (p = 0.75) and case detection both for susceptible TB (p = 0.31) and MDR-TB (p = 0.95) was not found statistically significant (see Table 2).

Discussion
We set out to compare the tests done and TB cases detected by Xpert1 MTB/RIF to Xpert1 MTB/RIF Ultra assay in Uganda. Xpert1 MTB/RIF Ultra was found neither to produce significantly more tests nor more tuberculosis cases either DS-TB or RR-TB when compared to Xpert1 MTB/RIF assay. The switch from Xpert1 MTB/RIF to Xpert1 MTB/RIF Ultra did increase the percentage of tests done using the same platform by up to 19%. However, given the significant reduction in the turn-around time of the Xpert1 MTB/RIF Ultra compared to the Xpert1 MTB/RIF [9], we expected an increment of up 50% [10]. We do recommend that the health systems approach is used to elucidate all the probable potential of the MTB/RIF Ultra assay. There were 7 TB cases per 1000 tests done reduction on changing the assay from Xpert1 MTB/RIF to Xpert1 MTB/RIF Ultra. A modeling mathematical study had found the reverse, and it anticipated 2 to 9 TB cases per 1000 individuals tested when the switch was made from MTB/RIF to Xpert1 MTB/RIF Ultra [12]. Therefore, we recommend further studies to evaluate the incremental benefit of switching from Xpert1 MTB/RIF Ultra to Xpert1 MTB/RIF in detecting additional TB cases.
Xpert1 MTB/RIF Ultra did detect the same number of MDR-TB cases as Xpert1 MTB/ RIF. This agrees with most of the systematic reviews comparing Xpert1 MTB/RIF and Xpert1 MTB/RIF Ultra. However, given that over 70% of the estimated RR-TB cases are still being missed globally [13], there is a need for a faster point of care, with a high sensitivity to diagnose RR-TB accurately thereby increasing the number of people diagnosed with RR-TB, hence reducing its associated morbidity and mortality.
This study was not without limitations, some data which could have been beneficial in the comparison (Number with DS-TB and RR-TB who are either children or HIV positive) since Xpert1 MTB/RIF was found to have a significant difference in these sub-groups were not available. It also could have been beneficial to compare smear positive and smear-negative samples, since Xpert1 MTB/RIF Ultra is more sensitive in the latter, more TB cases would have been detected. We also did not control for the historical threats to validity which could have been caused by a change in management and personnel. However, this was minimized by reducing the time between the before and after periods,